**Multilingual, non-autoregressive System 1 decision model.** Give it a **state** (text, email, ticket, or JSON) and **typed questions**; it returns typed answers with mathematically calibrated probabilities in a single forward pass (~33 ms) across 100+ languages. Trained with reinforcement learning against strictly proper scoring rules (**RLCD**), so reporting honest probabilities is the only way to maximise reward. It never generates text, so there is nothing to parse and nothing to hallucinate. Python 3.10 or newer. Optional extras: `laya[serve]` (HTTP server), `laya[mcp]` (MCP server), `laya[langchain]` (LangChain and LangGraph), `laya[onnx]` (ONNX Runtime), `laya[fast]` (TileLang GPU fast path). Platform-by-platform setup is in the GitHub README. **Long documents.** `laya-multilingual` reads up to 8,192 tokens with `max_len=8192`. Measured accuracy and time by document length (benchmark script):

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laya-multilingual

convaiinnovations

Non-autoregressive **System 1 decision model** covering 100+ languages. Give it a **state** (text, email, ticket, or JSON) and **typed questions**; it returns typed answers with probabilities in a single forward pass. No text generation, so nothing to parse and nothing to hallucinate. Part of the Laya family — **use this checkpoint for anything that is not English.** > **Long documents: `laya-multilingual` reads up to 8,192 tokens.** It ships with a 1,024-token limit that cuts long documents off, so pass `max_len=8192` for them: > > > > In the table below, 16 to 18 of 20 requests were answered correctly with up to about 4,000 tokens of text before them; beyond that results vary (8 to 17 of 20), so check long-document accuracy on your own data. Short inputs give identical answers with `max_len=8192`, and speed follows the input's real length, not the limit: short inputs are unchanged, and a 4,000-token input takes about 1.7 s on an Apple GPU.

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laya-typed-decisions

convaiinnovations

Non-autoregressive **System 1 decision model**, fine-tuned on the typed-decisions workflows: agent-trace observability, customer service, invoice processing and security incidents. **+3.9 points over Jev's published 0.727, above the 0.735 teacher ceiling**, with 2.4x better Brier and 1.6x better score MAE. Jev figures are third-party published, not measured here — there is no TypeSafe API access in this project, and sample sizes and prompts differ. Treat the comparison as indicative.

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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